Business warehouse arrangements are becoming increasingly popular as enterprises aggregate data from disparate sources. Information stored in business warehouses may be used, for example, by tools that visualize such data for analytical purposes (e.g., pivot tables, slice and dice mechanisms, drilldown capabilities, etc.) or for presentation purposes (e.g., charts, cockpits, etc.).
Data from within a business warehouse may be obtained in response to an on-line analytical processing (OLAP) query. OLAP queries may retrieve subsets of data stored within a business warehouse. Queries may also be used to provide supporting infrastructure such as aggregation, evaluation of conditions, and calculation of additional number not directly provided by the business warehouse.
In order to facilitate responses to an OLAP query, the underlying data is stored in a multi-dimensional cube format. Each axis of the cube contains identifiers from a field column in a database table and is referred to as a dimension. For example, a first dimension may contain product names, a second dimension may contain store names, and a third dimension may contain measures relating to the various products and stores (e.g., volume, cost, revenue, etc.). In most cases, a dimension is comprised of the same class of objects.
The outputs resulting from OLAP queries are typically complex result sets that may consist of information for row headers, column headers, and all data cells. If an OLAP query result is too complex, business processes may be disrupted and processing resources may be unnecessarily consumed.